Boosting Efficiency, Resilience, and Security: AI in Energy Infrastructure Management

In the UAE, the Abu Dhabi National Oil Company (ADNOC) has embraced AI in its operations. ADNOC’s recent deployment of AI-enabled digital operations at offshore Satah Al Razboot (SARB) field has resulted in a significant production capacity increase of 25%.

Energy infrastructure, the backbone of the power sector, encompasses a vast array of components — from extensive networks of oil and gas pipelines to power lines, storage facilities, and other related structures. As the global demand for energy grows, these infrastructures bear the brunt of ensuring adequate power is supplied. As the International Energy Forum, the global energy infrastructure must become “more secure, sustainable, and resilient.”

However, managing such a complex infrastructure is no walk in the park. 

Beyond the physical assets, operators must also navigate a range of unpredictable challenges, including natural disasters and cyber threats, all while striving for efficiency. Traditionally, energy infrastructure management has relied heavily on manual processes and reactive maintenance strategies. However, artificial intelligence (AI) is revolutionising this approach, enabling more proactive and predictive management methods.

Predictive maintenance and operational efficiency

One of the most promising applications of AI in energy infrastructure is predictive maintenance. AI tools can analyse data — lots of data — and predict when equipment is likely to fail or require maintenance. This allows operators to address issues before they lead to costly breakdowns or outages.

In the UAE, the Abu Dhabi National Oil Company (ADNOC) has embraced AI in its operations. ADNOC’s recent deployment of AI-enabled digital operations at offshore Satah Al Razboot (SARB) field has resulted in a significant production capacity increase of 25%. Located 120 kilometers northwest of Abu Dhabi, the SARB field utilises digital solutions to operate remotely from Zirku Island, which is 20 kilometers away. The integration of remote monitoring and smart technologies at the control centre enables real-time decision-making. 

In June, Sultan Al Jaber — who serves at the country’s Minister of Industry and Advanced Technology, ADNOC’s CEO, Masdar’s Chairman, and COP28 President — also highlighted how the country’s “power companies are using neural networks to mitigate the intermittency and storage challenges of renewable energy by forecasting weather patterns and preempting peaks and dips in usage.” 

The UAE is located in the Middle East, a region where oil refineries run aplenty. In the region, AI is poised to play a critical role in optimising refinery infrastructure. By detecting where structures must be drilled, enhancing safety, improving efficiency, and optimising complex processes, AI helps refineries minimise downtime, reduce operational costs, and adhere to stringent quality standards.  

Demand response and renewable energy management

Beyond maintenance, AI also improves demand response management (DRM) systems, which involve adjusting electricity consumption in response to supply conditions (i.e., during peak demand periods).

AI algorithms can evaluate vast amounts of data to predict demand patterns and automatically adjust the distribution of power across the grid. This capability helps prevent overloading and blackouts while allowing for more efficient use of energy. Ultimately, it reduces the the need for expensive and carbon-intensive peaking power plants.

This enhancement in DRM systems — coupled with AI’s predictive maintenance capabilities — is particularly advantageous in the context of renewable energy. These features of AI can ensure the reliability of wind turbines and solar panels, which are often situated in remote and harsh environments. 

For example, AI can analyse vibration data from turbines or thermal images from solar panels to detect early signs of wear or malfunction.

On cybersecurity

Along with the digitisation of energy infrastructure comes more vulnerability to cyber threats. AI can also play a vital role in boosting the security of these critical systems. AI can help monitor network traffic and identify unusual patterns, detecting potential cyber-attacks in real-time. This enhanced security extends to the physical realm. AI can secure physical infrastructure by monitoring video feeds from critical facilities, pinpointing potential threats, and triggering alerts for human intervention.

With all these benefits, AI is on track to redefine energy infrastructure management as we know it. 

“As we move to implementation, the world must leave no stone unturned to accelerate progress. Specifically, that means embracing artificial intelligence, which promises to have a far-reaching, transformational impact on the energy transition and is projected to add $7 trillion to global GDP over the next 10 years,” Al Jaber remarked.